# Lexiq Reader 3B **Fine-tuned from [Jina AI's ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2)** ## Overview Lexiq Reader 3B is a specialized 1.5B parameter language model optimized for converting raw HTML into clean, structured markdown and JSON. This model is fine-tuned from Jina AI's ReaderLM-v2 for enhanced performance in document processing pipelines. ## Model Details - **Base Model**: ReaderLM-v2 (Qwen2.5-1.5B architecture) - **Parameters**: 1.54B - **Context Window**: Up to 512K tokens - **Supported Languages**: 29 languages including English, Chinese, Japanese, Korean, French, Spanish, Portuguese, German, Italian, Russian, Vietnamese, Thai, Arabic - **License**: CC-BY-NC-4.0 ## Key Features - **HTML to Markdown**: Converts complex HTML with tables, lists, code blocks, and LaTeX - **HTML to JSON**: Direct extraction using predefined schemas - **Long Context**: Handles documents up to 512K tokens - **Multilingual**: Comprehensive support across 29 languages - **Optimized for Production**: Enhanced stability for long-form content generation ## Quick Start ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # or "cpu" tokenizer = AutoTokenizer.from_pretrained("remodlai/lexiq-reader-3b") model = AutoModelForCausalLM.from_pretrained("remodlai/lexiq-reader-3b").to(device) # Create prompt html = "

Hello, world!

" messages = [{"role": "user", "content": f"Extract the main content from the given HTML and convert it to Markdown format.\n```html\n{html}\n```"}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) # Generate inputs = tokenizer.encode(prompt, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08) print(tokenizer.decode(outputs[0])) ``` ## Fine-tuning Details This model has been fine-tuned for: - Enhanced document structure preservation - Improved handling of technical documentation - Better extraction of code snippets and API documentation - Optimized for multimodal RAG pipelines ## Deployment ### Modal See deployment examples in the `modal/` directory for serverless deployment with auto-scaling. ### vLLM For high-throughput inference: ```python from vllm import LLM, SamplingParams llm = LLM(model="remodlai/lexiq-reader-3b", max_model_len=256000, dtype='float16') sampling_params = SamplingParams(temperature=0, top_k=1, max_tokens=8192) ``` ## Hardware Requirements - **Minimum**: T4 GPU (16GB VRAM) - **Recommended**: RTX 3090/4090 or A10G for optimal performance - **Memory Usage**: ~3GB model weights + KV cache ## Credits This model is based on [ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2) by [Jina AI](https://jina.ai/). ## License CC-BY-NC-4.0 - Non-commercial use only